We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by imposing a Gaussian prior over value functions and assuming a Gaussian noise model. Due to the Gaussian nature of the random processes involved, the posterior distribution of the value function is also Gaussian and is t...

the statistical analysis of spatial data is usually done under gaussian assumption for the underlying random field model. when this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. one of the crucial problems in this setting is specifying the block sizes. in this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...

When faced with a limited budget to collect data in support of a multiple attribute selection decision, the decisionmaker must decide how many samples to observe from each alternative and attribute. This allocation decision is of particular importance when the observation process is uncertain, such as with physical measurements. For example, when the U.S. Department of Homeland Security must de...

A novel efficient methodology for probabilistic material reliability analysis considering fine-scale microstructure stochasticity is proposed in this paper. Integrated computational engineering requires multiscale capabilities to enable design and validation. Two critical challenges are identified: handling uncertainties from microstructures properties; the “curse of dimensionality” solvers. Th...

Sequential methods for face recognition rely on the analysis of local facial features in a sequential manner, typically with a raster scan. However, the distribution of discriminative information is not unifom over the facial surface; for instance the eyes and the mouth are more informative than the cheek. We propose an extension to the sequential approach, where we take into account local feat...

Because of random nature of many dependent variables in coastal engineering, treatment of effective parameters is generally associated with uncertainty. Numerical models are often used for dynamic analysis of complex structures, including mechanical systems. Furthermore, deterministic models are not sufficient for exact anticipation of structure’s dynamic response, but probabilistic models...

The ease of estimating the uncertainties numerical simulations in metal forming is particular interest. This uncertainty arises from, for example, material parameter identification, geometric dimensions, external loads, and contact conditions. In this paper, we aim to address issue with extension influences, boundary as well friction quantification from identification – here, terms sensitivitie...